Method for optimizing plastic compositions used in packaging to increase shelf-life of perishable products and a system thereof

ABSTRACT

The present invention relates to relates to a method of optimizing a plastic composition formed from a plurality of resin feedstocks. A plurality of resin feedstocks are provided. The plurality of resin feedstocks are blended to form the plastic composition. One or more properties of the plastic composition, including radiation absorption, radiation transmission, gas evolution, radiation fluorescence, or melting properties, are measured. The ratio of the plurality of resin feedstocks being blended into the plastic composition is adjusted, based on said measuring, to form an optimized plastic composition. A system for performing the method is also disclosed.

This is a divisional application of U.S. patent application Ser. No.15/491,201, filed Apr. 19, 2017, which claims the benefit of U.S.Provisional Patent Application Ser. No. 62/324,790, filed Apr. 19, 2016,which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present application relates to methods and systems for optimizingplastic compositions formed from resin feedstocks, and more specificallyto optimal blending or the use of modifiers/additives in recycledthermoplastic in packaging to increase shelf-life of perishableproducts.

BACKGROUND OF THE INVENTION

In recent years, federal and state legislation has been proposed toincrease the use of sustainable (bio-based) and recycled packagingmaterials. The adoption of recycled plastic or bio-plastic alternativeshas been slow due to contaminant loads, the costs associated withcollection and sorting, and overall quality. Bio-plastics have beenviewed as a costly alternative to traditional plastics. Thus, there is aneed to incentivize the use of bioplastics and recycled packagingmaterials.

Agricultural growers and packers utilize plastic packaging for retailconvenience foods and food service products and are currently thelargest users of plastic film and sheeting in the United States. Studieshave indicated that the vitamin content of current fruits and vegetablesis significantly lower than 50 years ago. This is important as mostAmericans do not eat enough of these products.

It has been suggested that the increased haze, scattering, and filteringof light in recycled and bio-plastic substrates reduces the rate ofproduct degradation. As such, recent initiatives by retailers havecalled for an increase in the use of recycled packaging substrates.However, the nature of recycled packaging material, which includesvariance in the overall composition, makes it difficult to predict howeffective the final plastic product will be at providing these benefits.Further, once the product is finalized, there is no opportunity toadjust the composition to provide the maximum benefit with respect toincreasing the shelf-life of perishable products.

The present invention is directed to overcoming these and otherdeficiencies in the art.

SUMMARY OF THE INVENTION

One aspect of the present invention relates to a method of optimizing aplastic composition formed from a plurality of resin feedstocks. Aplurality of resin feedstocks are provided. The plurality of resinfeedstocks are blended to form the plastic composition. One or moreproperties of the plastic composition, including radiation absorption,radiation transmission, gas evolution, fluorescence, or meltingproperties, are measured. The ratio of the plurality of resin feedstocksbeing blended into the plastic composition is adjusted, based on themeasured one or more properties, to form an optimized plasticcomposition.

Another aspect of the present invention relates to a system including ablending apparatus configured to blend a plurality of resin feedstocks.One or more sensors are positioned at different locations in theblending apparatus to measure one or more properties of the plurality ofresin feedstocks, including radiation absorption, radiationtransmission, photoionization, or melting properties. The system furtherincludes a computing device comprising a processor and a memory coupledto the processor which is configured to execute one or more programmedinstructions stored in the memory and comprising receiving measurementsof the one or more properties of the plurality of resin feedstocks fromthe one or more sensors and outputting one or more instructions to theblending apparatus to blend the plurality of resin feedstocks based onthe measured one or more properties to optimize the plastic composition.A compound delivery system is configured to blend the plurality of resinfeedstocks based on the one or more properties.

This technology provides increases in nutritive retention andshelf-life, as well as perceived freshness of vegetables, when utilizingrecycled and bio-plastics of varying composition. Many compounds havebeen identified as contributing to the light filtering effect seen inrecycled plastics. The present technology allows for the presence ofthese compounds to be modified by optimal blending or by using additivess part of the recycling process or during conversion and manufacturingto optimize the resultant plastic composition to provide extendedshelf-life for products stored therein.

Utilizing the present technology, various radiation, absorption,emission, fluorescence, and thermal properties of plastic compositionsare measured during the conversion of resin feedstocks to a plasticcomposition. The measured properties provide data that may be utilizedto adjust the blending of the resin feedstocks or to add additionalmaterials to the blend to optimize the resultant plastic composition toprovide longer shelf-life and reduce degradation for products stored inthe plastic composition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary environment including a computing device coupledto one or more sensors to optimize or increase performance of a plasticcomposition in accordance with the present technology.

FIG. 2A shows a side view of an exemplary extrusion system.

FIG. 2B shows a side view of another exemplary extrusion system.

FIG. 3 is a flow chart of an exemplary method for optimizing a plasticcomposition made from feedstock resins.

FIGS. 4A and 4B illustrate a UV-Vis spectra (FIG. 4A) and subtractionspectra (x % RPET−0% RPET) (FIG. 4B) of polyethylene terephthalate sheetcomprising a varying amount of post-consumer recycled content possessingincreased UV radiation absorption. Example nutrients and theirrespective degradation wavelengths are included for comparison.

FIG. 5 illustrates a reaction pathway of diethylene glycol constituentunder thermos-oxidative conditions of poly(ethylene terephthalate) andsubsequent hydroxyl radical reaction with terephthalic acid constituentsproducing quinone derivatives.

FIG. 6 illustrates a β-scission reaction of poly(ethylene terephthalate)under thermo-oxidative conditions producing alkene and aldehydefunctional groups.

FIGS. 7A and 7B illustrate subtraction attenuated total reflectanceFourier transform infrared spectra of poly(ethylene terephthalate)containing post-consumer recycled content of the carbonyl region (FIG.7A) and the aromatic ring torsion region (FIG. 7B).

FIGS. 8A and 8B illustrate fluorescence emission spectra of polyethyleneterephthalate sheets as a function of percent post-consumer content(FIG. 8A) and the correlation of absorbance at 350 nm with thefluorescence intensity at 501 nm (ex 350) (FIG. 8B).

FIG. 9 illustrates example changes in thermal properties of PET due toincorporation of post-consumer recycled content.

FIG. 10 illustrates changes in ultraviolet visible spectrum absorptionof PET due to incorporation of post-consumer recycled content.

FIG. 11 is graph illustrating color rating vs. days of storage.

FIGS. 12A-12 C illustrate a UV-Vis spectra of polyethylene terephthalatesheets with increased PCR-PET content (FIG. 12A), a change in UVAabsorbance potential from virgin PET resin for PCR-PET blends for datacollected in 2016 and 2011 (FIG. 12B), and a graph showing a linearrelationship between the thickness normalized absorbance at 350 nm andthe UVA absorption potential (FIG. 12C).

FIGS. 13A-13E are graphs showing calculated peak area changes due toincreasing PCR-PET blending with virgin PET material carbonyl peak arearesults (ATR-FTIR) (FIG. 13A), carbonyl peak area results (Raman) (FIG.13B), ester peak area (ATR-FTIR) (FIG. 13C), asymmetric ether peak arearesults (ATR-FTIR) (FIG. 13D), and C═C stretch (Raman) (FIG. 13E).

FIG. 14 is a graph showing virgin PET subtraction spectroscopy ofPCR-PET blends using ATR-FTIR of 730 to 710 cm⁻¹ region specific for(CH₂)n-CH₃ chain deformation, end group rotation, and aliphaticcrystallinity.

FIG. 15 shows fluorescence emission spectra (excitation wavelength of350 nm) of polyethylene terephthalate sheets as a function of percentpost-consumer content.

FIG. 16 shows peak fluorescent intensities from the 3D fluorescencescans. Intensities were recorded at an excitation of 335 nm and emissionof 395 nm.

DETAILED DESCRIPTION OF THE INVENTION

The present application relates to methods and devices for optimizingplastic compositions formed from resin feedstocks, and more specificallyto the use of modifiers in recycled thermoplastic in packaging toincrease shelf-life of perishable products.

One aspect of the present invention relates to a system including ablending apparatus configured to blend a plurality of resin feedstocks.One or more sensors are positioned at different locations in theblending apparatus to measure one or more properties of the plurality ofresin feedstocks, including radiation absorption, radiationtransmission, photoionization, or melting properties. The system furtherincludes a computing device comprising a processor and a memory coupledto the processor which is configured to execute one or more programmedinstructions stored in the memory and comprising receiving measurementsof the one or more properties of the plurality of resin feedstocks fromthe one or more sensors and outputting one or more instructions to theblending apparatus to blend the plurality of resin feedstocks based onthe measured one or more properties to optimize the plastic composition.A compound delivery system is configured to blend the plurality of resinfeedstocks based on the one or more measured properties.

An exemplary environment 10 including a computing device 12 coupled toone or more sensors 14(1)-14(n) and a compound delivery system 15 by acommunication network 18 is illustrated in FIG. 1. While not shown, theenvironment also may include additional components whose connections andoperations are well known to those of ordinary skill in the art and thuswill not be described here. Sensors 14(1)-14(n) are coupled to ablending apparatus, such as an extrusion system 16, although sensors14(1)-14(n) may be coupled to other blending apparatuses such as aninjection system or a resin conversion system, by way of example.Sensors 14(1)-14(n) are positioned at different locations in extrusionsystem 16, such as locations 102-109 as illustrated in FIG. 2. Thistechnology provides a number of advantages including a system andmethods that more effectively optimize plastic compositions formed froma plurality of resin feedstocks. By way of example, the plasticcompositions may be optimized to isolate and control absorption,transmission, or emission of electromagnetic wavelengths to improve theconsumer performance of food or beverage containers constructed from theplastic compositions. The food or beverage containers constructed fromthe optimized plastic compositions advantageously provide for increasedshelf-life and reduced product and nutrient decay of items storedtherein.

Referring again more specifically to FIG. 1, computing device 12 in thisexample is configured to be capable of receiving measurements of one ormore properties of the plurality of resin feedstocks from sensors14(1)-14(n) and outputting one or more instructions to compound deliverysystem 15 or extrusion system 16 to blend the plurality of resinfeedstocks based on the measured properties in order to optimize theplastic composition, as illustrated and described with examples of themethods described herein. Computing device 12 includes at least aprocessor 20, a memory 22, a communication interface 24, an input device26, and a display device 28, which are coupled together by a bus 30 orother communication link, although other numbers and types of systems,devices, components, and elements in other configurations and locationscan be used.

Processor 20 in computing device 12 executes a program of instructionsstored in the memory for one or more aspects of the present technology,although other numbers and types of systems, devices, components, andelements in other configurations and locations can be used.

Memory 22 in computing device 12 stores the programmed instructions forone or more aspects of the present technology, although some or all ofthe programmed instructions could be stored and/or executed elsewhere. Avariety of different types of memory storage devices, such as a randomaccess memory (RAM), read only memory (ROM), hard disk, CD ROM, DVD ROM,or other computer readable medium which is read from and written to by amagnetic, optical, or other reading and writing system that is coupledto processor 20, can be used for memory 22.

Communication interface 24 of the computing device 12 is used tooperatively couple and communicate between the computing device 12 andsensors 14(1)-14(n) via the communications network 18, although othertypes and numbers of communication networks, systems, or other linkswith other types and numbers of connections and configurations can beused. By way of example only, communications network 18 could use TCP/IPover Ethernet and industry-standard protocols, including NFS, CIFS,SOAP, XML, LDAP, and SNMP, although other types and numbers ofcommunication networks, such as a direct connection, a local areanetwork, a wide area network, modems and phone lines, e-mail, andwireless communication technology, each having their own communicationsprotocols, can be used.

Input device 26 and display device 28 of computing device 12 enable auser to interact with computing device 12, such as to input and/or viewdata and/or to configure, program, and/or operate computing device 12.Input device 26 may include a keyboard, computer mouse, and/or touchscreen and display device 28 may include a computer monitor, althoughother types and numbers of input devices and/or display devices couldalso be used in other examples. Input device 26, by way of example, maybe utilized to manually input data regarding one or more properties ofthe plastic composition.

Sensors 14(1)-14(n) may be any sensors known in the art capable ofmeasuring one or more properties of a plastic composition in accordancewith the examples of methods illustrated and described herein. Althougha plurality of sensors 14(1)-14(n) are illustrated, it is to beunderstood that a single sensor could be utilized in some examples. Byway of example, sensors 14(1)-14(n) are configured to measure radiationabsorption, radiation transmission, gas evolution, radiationfluorescence, or melting properties of a plastic composition.

Sensors 14(1)-14(n) may be configured to perform, by way of example,ultraviolet-visible spectroscopy analysis, an attenuated totalreflectance Fourier transform infrared spectroscopy analysis, adifferential scanning calorimetry analysis, a mechanical analysis, x-rayfluorescence analysis, or energy dispersive x-ray fluorescence analysisof the plastic composition. It is to be understood that sensors14(1)-14(n) are not limited to these measurement techniques, and otheranalytical techniques may be employed that are suitable for obtainingone or more properties of a plastic composition.

In one example, an ultraviolet visible spectrometer may be utilized. Theultraviolet visible spectrometer must have sufficient resolution togenerate a spectrum of absorbance as a function of the wavelength of theincident irradiation between 300 nm and 400 nm. The range of absorptionmeasurement analysis may be lower than 300 nm and higher than 400 nmdepending on the desired absorption properties of the product. Thethickness of the specimen analyzed (perpendicular to the incidentirradiation) is measured as the thickness of the specimen affects themeasured absorption properties. A single wavelength absorptionmeasurement may be sufficient if the data represents the desiredultraviolet/visible absorption properties or under rapid throughputconditions for quality control.

In another example, Fourier transform infrared spectroscopy is utilized.A background spectrum is collected and stored. The minimum number ofscans collected for the background spectrum will be considered as thenumber of scans that generates a representative spectrum of thebackground environment. The collected spectra may be normalized (i.e.,the spectrum is multiplied by a number) to the intensity of a wavenumberthat is representative of a characteristic band of the polymer that doesnot change in intensity due to a chemical reaction. In addition,transmission experiments may be normalized by the path length of thespecimen (typically the thickness of the specimen). In certain cases, amathematical subtraction may be performed to emphasize spectral shiftsaccording to equation 1.

A(v)_(subtracted spectrum) =A(v)_(spectrum A) −A(v)_(spectrum B)  [1]

In yet another example, fluorescence intensity measurements are madewith an instrument capable of irradiating the sample with ultravioletand visible light and detecting the fluorescence response. Theinstrument must at minimum be able to irradiate the sample at 350 nmexcitation wavelength, however, other excitation wavelengths may be usedfor analysis. The fluorescence detector must at minimum be able todetect the intensity of the fluorescence emission at 501 nm, however,the fluorescence intensity of other emission wavelengths may be used foranalysis so long as the data are indicative of the properties indicatedin this invention. In some cases, a “3D” scan can be employed where thefluorescence intensity of at a given wavelength for a specificexcitation wavelength for multiple sequential excitation wavelengths ata known interval.

Sensors 14(1)-14(n) are located at positions 102-109 in extrusion system16 as illustrated in FIG. 2A to measure the properties of the plasticcomposition at various points in the extrusion process. In one example,the measuring is carried out at a single point in time followingblending of the plurality of resin feedstocks to form the plasticcomposition. In another example, the measuring is carried out at leasttwo different points in time following blending of the plurality ofresin feedstocks to form the plastic composition. More specifically, byway of example only, a UV-Vis measurement and X-ray fluorescenceelemental analysis may be performed on the extruded sheet between thefirst and second rollers on the take up apparatus as illustrated in FIG.2. Sensors 14(1)-14(n) communicate the measured properties of theplastic composition to computing device 12 via communication network 18.

Compound delivery system 15 is coupled to extrusion system 16 and isconfigured to blend the plurality of resin feedstocks in the extrusionsystem 15 based on the one or more properties of the plastic compositionmeasured by sensors 14(1)-14(n) as illustrated in FIG. 2A. By way ofexample, compound delivery system 15 is one or more of a co-extruder, adosing pump, or a direct intake to an extrusion line of extrusion system16. In another example, as illustrated in FIG. 2B, a plurality of feedpumps 1-3 may be utilized. In one example, compound delivery system 15adjusts the ratio of the feedstock resins in extrusion system 16. Inanother example, compound delivery system 15 is configured to add one ormore additive compounds to the plastic composition during the extrusionprocess. Compound delivery system 15 is manually controlled to adjustthe blending of the plurality of feedstock resins. Alternatively,compound delivery system 15 is part of an automatic feedback loop withthe adjusting of the blending of the plurality feedstock resinscontrolled by computing device 12, as described herein.

Although an example of environment 10 including computing device 12 isdescribed herein, it is to be understood that the devices and systems ofthe examples described herein are for exemplary purposes, as manyvariations of the specific hardware and software used to implement theexamples are possible, as will be appreciated by those skilled in therelevant art(s).

Aspects of the examples may also be embodied as a non-transitorycomputer readable medium having instructions stored thereon for one ormore aspects of the present technology as described and illustrated byway of the examples herein, as described herein, which when executed bya processor, cause the processor to carry out the steps necessary toimplement the methods of the examples, as described and illustratedherein.

Another aspect of the present invention relates to a method ofoptimizing a plastic composition formed from a plurality of resinfeedstocks. A plurality of resin feedstocks are provided. The pluralityof resin feedstocks are blended to form the plastic composition. One ormore properties of the plastic composition, including radiationabsorption, radiation transmission, gas evolution, radiationfluorescence, or melting properties, are measured. The ratio of theplurality of resin feedstocks being blended into the plastic compositionis adjusted, based on the measured one or more properties, to form anoptimized plastic composition.

An exemplary method for optimizing a plastic composition in accordancewith the present technology will now be described with respect of FIGS.1-3.

In step 300, a plurality of resin feedstocks are provided. In oneexample, the resin feedstocks are thermoplastic resin feedstocks, suchas polyethylene terephthalate (PET), although other thermoplastic resinfeedstocks such as polyethylene, polypropylene, polystyrene, poly methylmethacrylate, polycarbonate, an addition polymer, a condensationpolymer, and mixtures thereof, may be utilized. The plurality of resinfeedstocks may include a recycled polymeric material and a virginpolymeric material, although in one example only recycled polymericmaterial is utilized. The plurality of resin feedstocks may be providedin various combinations depending upon the application.

Next, in step 302, the plurality of resin feedstocks are blended to formthe plastic composition. The plurality of resin feedstocks are blendedin a blending apparatus, such as extrusion system 16 illustrated in FIG.2, although other blending apparatuses such as an injection system or aresin conversion systems, by way of example, may be utilized. Methods ofblending resin feedstocks to form a plastic composition are well knownin the art and will not be described herein.

In step 304, one or more properties of the plastic composition aremeasured by sensors 14(1)-14(n) coupled to extrusion system 16. Althougha plurality of sensors 14(1)-14(n) are illustrated, it is to beunderstood that a single sensor could be utilized at single measurementlocation. The one or more measured properties of the plastic compositioninclude one or more of radiation absorption, radiation transmission, gasevolution, radiation fluorescence, or melting properties. In oneexample, the measuring in step 304 is carried out at least two differentpoints in time following the blending in step 302. The measuring in step304 may be performed using ultraviolet-visible spectroscopy analysis, anattenuated total reflectance Fourier transform infrared spectroscopyanalysis, a differential scanning calorimetry analysis, a mechanicalanalysis, x-ray fluorescence analysis, or energy dispersive x-rayfluorescence analysis. It is to be understood that the measuringperformed is not limited to these measurement techniques, and otheranalytical techniques may be employed that are suitable for obtainingone or more properties of a plastic composition. In one example, themeasuring is performed by sensors 14(1)-14(n), which may be located atvarious locations along extrusion system 16, such as locations 102-109illustrated in FIG. 2.

Next, in step 306, computing device 12 receives the measured propertiesfrom sensors 14(1)-14(n) through communication network 18.Alternatively, the one or more properties may be measured by sensors14(1)-14(n) and entered manually into computing device 12 through inputdevice 26. Computing device 12 may receive data from sensors 14(1)-14(n)at multiple points in time during the conversion process.

In step 308, computing device 12 correlates the measured properties ofthe plastic composition to radiation transmission, radiation absorption,radiation emission, or radiation reflection properties for a completedplastic composition product having those measurements.

In step 310, computing device 12 identifies an adjustment to theblending of resin feedstocks or one or more additives to be added to theplastic composition to optimize these properties. More specifically, ifthe absorption properties desired are more similar to the absorptionproperties of a composition comprising 75% RPET and the measuredabsorption properties are more closely associated with 25% RPET, thenthe computing device 12 can alter the feed ratio of the resin feedstocksutilizing a calibrated single wavelength measurement (e.g., 350 nm) toadjust the composition ratio to achieve the desired absorption spectrumas described in FIG. 4A and FIG. 10. The general steps for identifyingan adjustment to the blending of resin feedstocks or one or moreadditives to be added is as follows: 1) utilize previously describedsensor data, such as spectral analysis, to determine a set of potentialindicators; 2) identify a set of indicators that are independent ofcontamination by sensors such as spectral analysis and other ionizationsensors; 3) use the set of indicators to determine samples with andwithout contamination loads and isolate indicators to sensor analysissuch as spectral characteristics; 4) utilize a series of algorithms foreach level of recycled content to interpret sensor signals and assigncontribution value of each indicator as a function of wavelengthisolation, filtering capacity, and shelf-life extension throughisolation and blocking of known vitamin degradation wavelengths

In step 312, the ratio of the plurality of resin feedstocks beingblended into the plastic composition is adjusted, based on saidmeasuring, to form an optimized plastic composition. The plasticcomposition is optimized based on radiation transmission, radiationabsorption, radiation emission, or radiation reflection properties. Theradiation transmission, radiation absorption, radiation emission, orradiation reflection properties are optimized so the optimized plasticcomposition isolates and controls electromagnetic wavelengths associatedwith one or more of vitamin degradation, adverse color changes,chlorophyll degradation, or degradation of other nutritional components.In one example, the plastic composition blocks between 50% to 75% moreof incident ultraviolet light compared to the plastic composition notcontaining recycled content at an exemplary thickness of 20 microns. Theoptimized properties allow for increased shelf-life of products storedin the plastic composition. By way of example, the blending of theplastic composition is optimized to isolate certain destructivewavelengths for particular vitamins/compounds as illustrated in Table 1below, which identifies wavelengths associated with nutrient degradationloss for the listed nutrients.

Table 1 below, which identifies wavelengths associated with nutrientdegradation loss for the listed nutrients.

TABLE 1 Wavelengths associated with nutrient degradation/loss for agiven nutrient UV (nm)/Visible Protective Vitamin/Compound (nm) CompoundVitamin A 330-350 Vitamin C (ascorbic acid) 265 Chlorophyll 280-400 429,659 Chlorophyll b 280-400 455, 642 Carotenoids (B-carotene, lutein,280-400 400-500 xanthophyll) Anthocyanins 278-313 517 Riboflavin(vitamin B2) 200-400 400-500 200-33 Myoglobin 200-300 570-590 200-300Tocopherol (vitamin E) 292

In one example, the blending is carried out using compound deliverysystem 15, such as a co-extruder, a dosing pump, or a direct intake toan extrusion line. Compound delivery system 15 may be located at any oneof the locations 102-109 in extrusion system 16 as illustrated in FIG.2. In one example, compound delivery system 15 is manually controlled toadjust the blending of the plurality of feedstock resins based on anoutput on display device 28 of computing device 12. Alternatively,compound delivery system 15 is part of an automatic feedback loop suchthat the adjusting of the blending of the plurality feedstock resins isdirectly controlled by computing device 12. In one example, compounddelivery system 15 adjusts the ratio of the feedstock resins inextrusion system 16. In another example, compound delivery system 15 isconfigured to add one or more additive compounds to the plasticcomposition during the extrusion process.

One or more additive compounds selected from the group consisting ofthermal or light stabilizers, antioxidants, plasticizers, fillers,nucleating agents, colorants, thermal conductors, catalysts, andcombinations thereof, are added to the plastic composition to providethe optimization in step 312. The one or more additives may be addedusing compound delivery system 15. Additive compounds are selected, byway of example, to increase light filtering to extend shelf-life.Utilizing this process allows manufacturers to control specificationsand monitor the direct or indirect addition of organic and inorganiccompounds to feedstock to allow for maximum shelf-life extension, whilemaintaining customer performance.

Example 1 Data Collection Using Ultraviolet Visible Spectroscopy

Ultraviolet radiation is broken down into three specific wavelengthsections: UVA, UVB, and UVC. Generally, UVC (100-280 nm) is completelyabsorbed by the atmosphere and UVB (280-315 nm) has a low depth ofpenetration due to their short wavelengths and therefore typically onlyresult in surface interactions. However, the wavelengths associated withUVA radiation (315-400 nm) are long enough to penetrate tissue andpossess sufficient energy to yield detrimental degradation. UV-Visspectra of the PET sheet absorbed more than the limits of the equipmentbetween 280 and 315 nm independent of RPET concentration, and thus, areexpected to perform similarly for absorption of UVB radiation. However,the absorbance of UVA radiation absorption increased with % RPETconcentration (FIG. 4A). The increased absorption of these wavelengthswould provide protection against UV degradation of nutrients. The areaunderneath the curve of the subtraction spectra (FIG. 4b ) is likely tobe related to the quantitative increase of UVA absorption and couldpossibly be used for incorporation into an empirical model.

Thermo-mechanical processing of polyethylene terephthalate is known tocause main chain degradation of the polymer producing a multitude ofdegradation byproducts. Thermo-oxidation of PET commonly occurs at thediethylene glycol constituent forming a peroxide compound. The resultinghydroxyl radical reacts with the benzene ring of the terephthalic acidconstituent producing quinone/hydroquinone derivatives which are knownto absorb UV irradiation (FIG. 5). _ENREF_1 Additionally, anotherdegradation reaction can produce alkene and aldehyde functional groupsvia β-scission of the carbon-oxygen bond adjacent to a carbonyl group(FIG. 6). Both alkene and aldehyde functional groups absorb UVradiation. As both degradation mechanisms produce UV absorbing species,blending post-consumer PET with virgin material will result in increasedUV absorption.

The loss of carbonyl groups detected by attenuated total reflectanceFourier transform infrared spectroscopy increased as a function ofincreasing recycled content (FIG. 7A). This is attributed to the loss ofacetaldehyde, a degradation byproduct, which is known to occur.Additionally, a broadening of the aromatic ring torsion band wasobserved and is attributed to the reaction of the hydroxyl radical withthe terephthalate constituent as discussed above (FIG. 7B).

As discussed above, degradation of PET occurs as a result ofthermomechanical processing. The mechanisms of degradation in theliterature propose the production of quinone derivatives on the aromaticring of the terephthalic acid constituent which are known to fluoresce.The increased fluorescence intensity between 470 and 570 nm is a resultof increased concentration of the fluorescing moiety, which would beproduced as a result of degradation events (FIG. 8A). This trend isexpected as increasing the PCR content of the sample would increase themass percent of PET that has undergone degradation events due toadditional melt processing cycles. A strong coefficient of determinationwas observed between the absorbance at 350 nm determined via UV-Visspectroscopy at the fluorescence intensity at 501 nm (e.g., 350 nm)suggesting that the moiety causing the increased absorbance at 350 nm isdue to the quinone derivatives formed during degradation processes (FIG.8B).

Preliminary Studies for Evaluation of Packaging and Shelf-Life Extension

Previous work indicated that green vegetables when packaged in recycledcontent PET containers and exposed to fluorescent light at 276 lux,yellowed and senesced at much faster rates than when stored under dimconditions at 36 lux (FIG. 9) and could be replicated using filteringmechanisms in recycled plastics (FIG. 11).

Freshly-harvested chives (Allium schoenoprasum L.) were obtained from acommercial grower in San Diego, Calif. Prior to shipment, the herbs wereplaced into polyethylene bags with perforations, then immediately cooledby forced air to approximately 4° C. The chives were placed in expandedpolystyrene-lined cardboard boxes with pre-cooled gel bags, and shippedvia overnight mail. Upon arrival, the herbs were held at 1±0.5 C, and90% RH. The herbs were used within twenty-four hours.

The chives were placed into non-vented, 907 ml produce containers whichwere subsequently covered with shrink-wrap film with moderate barrierproperties. Each packaging treatment was replicated 4 times with 3samples per replicate. A weight of 60±2 g of chives was placed into eachcontainer and sealed. Packages were placed into a constantly illuminated(fluorescence) controlled environment room held at 1±0.5 C. 90% RH.Samples were randomly placed within the chamber. Average light intensityvaried from 2760 lux at the top shelves to 750 lux at the middleshelves, to 340 lux at the bottom shelves of the chambers.

Weights for packages were recorded at the time they were placed intostorage. At one-week intervals, two randomly-chosen samples from eachtreatment were removed and weighed. Samples were compared with fresh,loose samples shipped from the supplier. Variables were ratedhedonically on a scale of 1-9, with 1 representing best quality and 9,complete breakdown and tissue destruction. Each package was evaluatedfor odor, color, wilt, and decay. Values were combined and averaged toproduce an overall quality rating. However, when the value for any onevariable exceeded 5, the product was considered to be no longermarketable.

Samples containing known amounts of recycled content can extendshelf-life up to 14 days (340 Lux vs 2760 Lux) utilizing filteringmechanisms and through recycled content formulation (FIG. 11).

Manufacture and Characterization of Recycled PET

Manufacture and Testing of Post-Consumer Recycled Sheet:

Recycled PET (RPET) of varying compositions and compounds weredried/recrystallized (<0.2%) using a Farragtech Card E seriesdryer/crystallizer (Farrag Tech GmbH; Wolfurt, Austria) and blended atapproximately 0, 20, 40, 60, 80, and 100% virgin-to-recycled contentwith varying compound loads as illustrated in Table 2 below. Eachconverted sheet using RPET blends with compositions of 0, 20, 40, 60,80, and 100% of virgin/recycled resin was extruded on a single-screwextruder (Davis-Standard, Pawcatuck, Conn.) to a final thickness of17-20 mil. Sheets were then formed into lidded punnets using a straightvacuum Formech Midi former (Formech Machine; Chicago, Ill.). Thethickness of each specimen was recorded at 12 random locations using adigital micrometer with a resolution of ±0.5 μm.

TABLE 2 Example compounds and loading affecting product quality andfreshness. Additive Amount PVC <500 ppm Color additive (black, TiOx)<1000 ppm Barrier Material (PETG) <6500 ppm Antimony <500 ppmAcetaldehyde By blend Boron <1000 ppm Nucleating agents <20% crystallinePolymers that phase separate Depends on size of phase Light stabilizersWI > 35 Antioxidants Master batch by blend Residual acids from <0.5g/in² adhesives

Determination of Thermal Properties of Manufactured Sheet:

Differential scanning calorimetry was used to determine the thermalproperties of the molded sheets (FIG. 9). Samples (3-6 mg) of eachPET/RPET sheet were added to an aluminum pan and hermetically sealed.Each study consisted of heat/cool/heat cycles between 30 and 310° C. ata rate of 10° C./min in accordance with ASTM D3418-03. Thecrystallization peak onset (Tc onset), crystallization temperature (Tc),crystallization peak offset (Tc offset), crystallization peak width (Tcwidth), heat of crystallization (ΔHc), percent crystallinity, meltingtemperature for the first heat cycle (Tm′), heat of melting for thefirst heat cycle (ΔHm′), glass transition temperature of the coolingcycle (Tg cool), glass transition temperature (Tg), onset of the meltingpeak for the second heat cycle (Tm″ onset), melting temperature of thesecond heat cycle (T″m), offset of the melting peak for the second heatcycle (Tm″ West), melting peak width of the second heat cycle (Tm″width), and heat of melting for the second heat cycle (ΔHm″) weredetermined using a TA Instruments calorimeter model DSC Q2000 (TAInstruments, DE, U.S.). The first scan was utilized to obtain the Tconset, Tc, Tc offset, Tc width, ΔHc, % crystallinity, Tm′, and AHm′. Thesecond scan was used to obtain the Tm″ onset, Tm″, Tm″ offset, Tm″width, and ΔHm″.

Determination of Light Absorption Properties of Manufactured Sheet:

Ultraviolet-visible (UV-Vis) spectroscopy was used to determine theabsorption properties of each PET/RPET sheet in the UV-Vis regions (FIG.10). Each sheet type was scanned between 200-700 nm using a ShimadzuPharmaspec UV-Vis Spectrometer (model UV-1700, Columbia, Md., U.S.) setto single scan mode at medium speed and sampling intervals of 1.0 nm.The haze (cloudiness) characteristics of the containers as a function ofrecycled content versus virgin and composition was determined using acommercial haze-gloss hazemeter (Altana, Wesel, Germany). Values werecompared to typical haze values for packaged products using RPET and PLAat ≤10% per ASTM D1003 (ASTM, 2000)

Food Packaging and Food Contact Compliance

Chemical Migration of Recycled Polyethylene Terephthalate (RPET):

Domestically manufactured, thermoformed polyethylene terephthalate (PET)containers with label claims of 0, 50, 70, or 100% post-consumer (PC)recycled water bottle content were collected from retail stores. Thefood simulants specified by 21CFR177.1630 that were used in this studywere n-heptane, 8% ethanol in water (v/v), and water. N-heptane (99%Optima grade, Fisher Scientific, Fair Lawn, N.J.) was used as receivedfrom the manufacturer. Deionized water (16.7 mega-ohm) was producedusing a Barnstead Nanopure II (Dubuque, Iowa) water purification system.A 95% ethanol solution (ACS Spectrophotometric grade, Acros Chemicals,Morris Plains, N.J.) was diluted to 8% (v/v) with nanopure deionizedwater.

Preparation of Samples:

The experimental design was a completely randomized 3×3 factorial. Acircular disk was cut from each package, slightly larger than the areaof exposure to ensure a good seal between the sample and the analysisapparatus: the area of exposure to the food simulants was 42.65 cm² foreach sample. Samples were conditioned according to ASTM D618-13 (ASTM2013) using a Thermo-Forma Scientific environmental chamber coupled toWatlow (Winona, Minn.) 982 series controllers. The mass of each disk wasdetermined using a Mettler Toledo (Columbus, Ohio) Model AB104 scalewith a resolution of +/−0.1 mg.

Compounds Contributing to Increased Nutritive Value Retention

Chemical Migration Analyses:

Samples were analyzed for chemical migration according to Title 21,Chapter 1, Subchapter B, Part 177.1630 of the Code of FederalRegulations (CFR 2014). This code requires that each package must notdesorb more than 0.5 mg of total chemicals per square inch (6.45 cm²)into specified food simulants: nanopure deionized water, 8% ethanolsolution in nanopure deionized water, and n-heptane.

Gas Chromatography-Mass Spectroscopy:

After exposure to the test samples, aliquots of the solvents wereinjected into a Clarus 600 GC (Perkin Elmer, Shelton, Conn.) equippedwith a 0.25 mm I.D., 30 m RTX 502.2 capillary column (Restek,Bellefonte, Pa.) attached to a Perkin Elmer Clarus 600C massspectrometer (MS). The helium carrier gas was set to a constant flowrate of 2.5 mL/min with the inlet port set to 280° C. The following oventemperature ramping protocol was used: initial temperature 35° C. for 5min, then increased to 150° C. at a rate of 30° C./min, then increasedto 250° C. at a rate of 20° C./min. The final temperature of 250° C. washeld for 10 min. Continuous mode MS at a rate of 5 scans/s between m/zratios of 35 and 300 was employed. Aliquots of pure solvent wereanalyzed by GC-MS to produce background spectra, the peaks of which wereeliminated from the spectra obtained from the food simulants in the21CFR177.1630 analysis.

Elemental Analysis by Inductively Coupled Plasma-Atomic EmissionSpectroscopy:

The PET/RPET sheets were analyzed by inductively coupled plasma-atomicemission spectroscopy (ICP-AES) for elemental composition. CaliforniaHealth and Safety Code 25214.13 states that lead, mercury, cadmium, andhexavalent chromium are contaminants of particular concern and areconsidered to be regulated metals. One specimen of each sheet wasanalyzed for elemental composition to confirm that introducingrecycled-PET into extruded sheets for food and cosmetic packagingapplications meets the requirements of the Toxics in PackagingPrevention Act (California Health and Safety Code 25214.13).

Compound Loading for Increased Freshness

Example compliment additives associated with recycled plastic andbiopolymer materials can be found below in ranges that provide optimallight, color and optical properties to increase freshness of productscontained.

Additive Amount PVC <500 ppm Color additive (black, TiOx) <1000 ppmBarrier Material (PETG) <6500 ppm Antimony <500 ppm Acetaldehyde Byblend Boron <1000 ppm Nucleating agents <20% crystalline Polymers thatphase separate Depends on size of phase Light stabilizers WI > 35Antioxidants Master batch by blend Residual acids from <0.5 g/in²adhesives

Example compounds and loading affecting product quality and freshness.

Compound Loading Adjustment Based on Absorption, Thermal and PhysicalBehavior

Compound loading to maximize performance and develop standards forrecycled thermoplastics (% RPET) was determined using the “best” subsetsof predictors of % RPET from a set of 17 potential indicators. Theanalysis proceeded in two steps: the first step identified the subset ofvariables that were independent of the silicone coating, a nuisancefactor. From the derived subset, the second step identified the bestsubsets of predictors of % RPET that maximize performance and ranges.

Determination of Independent Subset

The first task was to identify a subset of predictor variables that wereindependent of the nuisance factor, the silicone coating. For thispurpose, the silicone variable was scored 1=coating and 0=no coating andused as the dependent variable in a binary logistic model as described,by way of example, in Cox, D. R. “The Continuity Correction” Biometrika57:217-219 (1970) and Cox et al., Theoretical Statistics (Chapman andHall, London) (1974), the disclosures of which are hereby incorporatedherein by reference in their entirety. To accomplish this objective, theefficient score as disclosed in Rao, C. R., “Linear StatisticalInference and its Applications,” 2nd ed., New York: Wiley (1973), whichis hereby incorporated herein by reference in its entirety, was obtainedfor the 17 indicators (Table 3).

TABLE 3 Mean, SD, and Statistical Significance of the Coating TreatmentTable 1: Means, SD, and Statistical Significance of the CoatingTreatment Variable Mean SD λ-Score Prob. Hc 24.49 1.4 12.754 <0.001 Tg83.26 2.18 12.467 <0.001 Hm″ 30.77 3.38 11.756 <0.001 Tcwidth 13 2.077.815 0.005 Tm″offset 254.38 1.16 6.687 0.01 DEGContent 4.09 0.21 4.9020.027 Tm″ 248.5 1.18 4.902 0.027 TgReverse 74.82 1.65 3.544 0.06 Hm′34.83 2.13 2.879 0.09 Tcoffset 144.16 3.48 1.318 0.251 Tconset 131.161.91 0.895 0.344 Tm″onset 227.89 3.05 0.707 0.4 Crystal 8.99 1.71 0.4960.481 Tm′ 251.67 1.38 0.108 0.742 Tc 137.55 2.06 0.032 0.859 Tm″width26.49 2.5 0.03 0.863 A350 nm 1.56 0.15 0.003 0.958

Rao's efficient score, λ, measures the initial contribution a variablemakes in predicting an outcome; it is commonly used as an initial screenin forward selection algorithms. In typical forward selectionalgorithms, if a potential predictor has a sufficiently significantλ-score; it becomes eligible for further analysis. However, in thecurrent study the goal was to identify indicators that were independentof the nuisance factor; thus, non-significant linear combinations ofindicators were sought. In line with this objective, the eight variablesreported in Table 3 with λ-scores with p>0.10 were flagged aspotentially independent of the nuisance factor, the silicone coating. Toidentify the linear combination of variables, independent of a siliconecoating, the systematic “directed search” selection method of Daniel etal., “Fitting Equations to Data: Computer Analysis of Multifactor Data,”J. Wiley and Sons, New York, N.Y. (1980), the disclosure of which ishereby incorporated herein by reference in its entirety, was adapted asfollows: Variables with the smallest λ-scores (high p values) wereentered into the binary logistic regression one at a time. After eachentry, a xx2 test for linear dependence and the Cox-Snell RR2 werecalculated. This process stopped when a statistically significant linearcombination was obtained (Table 4).

TABLE 4 Variable Subsets and Test Statistics Predictor x² df prob. R²A350 nm 0.003 1 0.958 0.000 Tm″width 0.042 2 0.979 0.001 Tc 0.274 30.965 0.005 Tm′ 0.719 4 0.949 0.012 % Crystal 3.186 5 0.671 0.052Tm″Onset 18.859 6 0.004 0.270

The systematic test of nested variables revealed five variables thatwere independent of the nuisance factor: A350 nm, Tm″ width, Tc, Tm′,and % Crystal. The linear combination independence requirement breaksdown when the sixth variable, Tm″ onset, is entered in the binarylogistic regression, xx2=18.86, df=6, p=0.004, and the Cox-SnellRR2=0.27 (Table 4).

TABLE 5 Model Fit Statistics for All Possible Subsets Table 3: Model FitStatistics for All Possible Subsets Relative Deviance Scale x² AICc BICdf = 54 A350 nm Tm″width, Tc, 49.2 0.9 40.6 3.2 24.4 Tm′, Crystal df =55 A350 nm, Tm″width, Tc, Tm′ 49.3 0.9 40.7 1.3 15.9 A350 nm, Tm″width,52.0 0.9 42.6 4.0 18.6 Tc, Crystal A350 nm, T′width, 49.8 0.9 41.2 1.816.3 Tm′, Crystal A350 nm, Tc, Tm′, Crystal 55.0 1.0 43.7 7.0 21.6Tm″width, Tc, 99.3 1.8 89.1 51.3 65.9 Tm′, Crystal df = 56 A350 nm,Tm″width, Tc 52.3 0.9 43.0 2.4 10.2 A350 nm, Tm″width, Tm′ 50.0 0.9 41.30.0 7.9 A350 nm, Tm″width, Crystal 52.6 0.9 43.2 2.6 10.5 A350 nm, Tc,Tm′ 56.2 1.0 44.4 6.2 14.0 A350 nm, Tc, Crystal 55.8 1.0 44.7 5.8 13.6A350 nm, Tm′, Crystal 56.9 1.0 45.2 6.9 14.8 Tm″width, Tc, Tm′ 138.6 2.5118.2 88.6 96.5 Tm″width, Tc, Crystal 106.0 1.9 91.7 56.0 63.9 Tm″Width,Tm′, Crystal 158.6 2.8 145.7 108.6 116.5 Tc, Tm′, Crystal 113.7 2.0 99.463.7 71.5 df = 57 A350 nm, Tm″width 53.1 0.9 43.7 1.1 2.2 A350 nm, Tc57.0 1.0 45.5 5.0 6.2 A350 nm, Tm′ 58.8 1.0 46.3 6.8 7.9 A350 nm,Crystal 57.6 1.0 46.0 5.6 6.7 Tm″width, Tc 156.7 2.7 130.5 104.7 105.9Tm″width, Tm′ 408.4 7.2 335.5 356.4 357.6 Tm″width, Crystal 170.6 3.0153.7 118.7 119.8 Tc, Tm′ 209.3 3.7 169.7 157.3 158.4 Tc, Crystal 116.22.0 100.3 64.3 65.4 Tm′, Crystal 160.5 2.8 145.8 108.5 109.6 df = 58A350 nm 59.5 1.0 47.3 5.5 0.0 Tm′width 561.5 9.7 444.7 507.5 501.9 Tc218.6 3.8 174.9 164.6 159.1 Tm′ 515.2 8.9 409.6 461.2 455.7 Crystal170.7 2.9 153.9 116.7 111.1

Generalized Logit Model

Generalized linear modelling, GZLM, is a general method allowing forvarious model types. The current model is a generalized logit model, onewith a Binomial prior and a uniform observed data distribution. Theposterior is a mixture of the two distributions.

For the present data set (Yi, Xi, wi) the following definitions apply,i=1, . . . , n denotes the 60 observations, Yi is a prior binomial countcorresponding to % RPET, Xi is the vector of the predictor variables(Table 2), and wi is the observed data weight for criterion Yi. The goalof the analysis was to identify the “best” subsets of % RPET predictors.For this purpose the generalized logit model for extra-binomialvariation was employed.

The current experiment has six levels of Yi(% RPET): 0, 20, 40, 60, 80,100% and to denote the number of observations, i=1, 2, . . . , n. Yi isthe imaginary prior count of the number of “successes” out of mi=m=100trials. For each of the six levels of % RPET there are 10 observationsfor a total of n=60 observations. Following William's (1982) model II,the expected value and variance are given as:

E(Yi)=mπi  [2]

Var(Yi)=φwi ⁻¹ mπi(1−πi)  [3]

where φwi⁻¹ represents the extra-binomial variation, φ is given by[l+ρ(m−1)], and where ρ is a parameter that may be interpreted as thecorrelation between the binary components; wi⁻¹ is the scale weight. Weassume constant ρ for all proportions. The goal of the analysis was toidentify the “best” subsets of % RPET predictors. For this purpose, ageneralized logit model for extra-binomial variation was employed.

The generalized logit model is defined by two functions g and v and thelink function g(πi)=ηi is a monotonic differentiable link functionrelating the mean probability (or binomial proportion)

${\pi \; i} = {E\left( \frac{V_{i}}{m} \right)}$

to the linear predictor η=Xβ where X is an n×p design matrix, β is a p×1vector of regression coefficients. The variance function vv relates thevariance with the expected binomial count, mπi, to the variance byVar(Yi)=ϕw⁻¹v(πi) where ϕ=mφ. For the binomial distributionv(πi)=πi(1−πi) so the scale factor ϕ=1 and wi=1 under ideal binomialconditions. In this experiment, however, the observed values for Yi areuniformly distributed over six levels: 0, 20, 40 60, 80, and 100; thus,the observed data weight reflects a uniform distribution, wi=⅙.

The five potential predictors (Table 4) were studied in the followinganalysis. The generalized logit model was defined as the followinglink-linear function:

ηi=g(πi)=Σjβjxij, i=1, . . . ,n.  [4]

with the logit link function g such that,

$\begin{matrix}{{{E\left( {{Yi}/m} \right)} = {{\pi \; i} = {g^{- 1}\left( {\ln \left\lbrack \frac{\pi_{i}}{1 - \pi_{i}} \right\rbrack} \right)}}},} & \lbrack 5\rbrack \\{{Where},{{\sum{\beta \; {jxij}}} = {\ln \left( \frac{\pi_{i}}{1 - \pi_{i}} \right)}}} & \lbrack 6\rbrack\end{matrix}$

The Best Subsets

From the five eligible variables (Table 4), the next step identified the“best” subsets. The five variables give 26 subset models; each isevaluated using the Bayesian information criterion, BIC, and the finitesample corrected Akaike Information Criterion, AICc.

$\begin{matrix}{{{BIC} = {{{- 2}*(L)} + {k*{\ln \left( {n*m} \right)}}}},} & \lbrack 7\rbrack \\{{{AICc} = {{{- 2}*(L)} + \frac{2{k\left( {n*m} \right)}}{\left( {n*m} \right) - k - 1}}},} & \lbrack 8\rbrack\end{matrix}$

where L is the log likelihood function, kk=pp+1 is the number ofpredictors plus the intercept in the model, nn is the number ofobservations and mm is the number of trials. BIC and AICc were used asthe information criteria to evaluate the relative quality of thegeneralized logit models as disclosed in Akaike, H. “Maximum LikelihoodIdentification of Gaussian Autoregressive Moving Average Models,”Biometrika 60(2):255-265. (1973); Schwarz, G., “Estimating the Dimensionof a Model.” The Annals of Statistics 6(2):461-464 (1978); and Andersonet al., “Avoiding Pitfalls When Using Information-Theoretic Methods,”The Journal of Wildlife Management 66:912-918 (2002), the disclosures ofwhich are hereby incorporated herein by reference in their entirety.

The information criteria (Table 5) are given relative to the minimum,where min AICc=90.11 and min BIC=109.5; smaller relative scores indicatebetter fit. BIC and AICc tend to move together, but each emphasizesdifferent aspects of the model. BIC tends to favor models with lesspredictor variables (George, 2000). In the current data set, the bestmodel by BIC is the single predictor A350 nm, and the best model by AICcis the three variable model: A350 nm, Tm″ width, and Tm′. The best twovariable model is A350 nm and Tm″ width.

Assessment of Changes in Nutritive Value of Selected Vegetables

Converted sheet were thermoformed into retail packaging and used toassess changes in the nutritive value of specific vegetables duringprolonged storage. Changes were correlated to the clarity and permeationcharacteristics of the plastics. All packages were stored incontrolled-environment rooms equipped with 2 banks of fluorescentlights. Light intensity at the package surface was measured as Lux usinga quantum light meter. Nutritive changes were assessed as decreases inVitamin C, β-carotene, and chlorophyll during simulated retaildistribution and display, up to 21 days after packaging.

Ascorbic Acid (Vitamin C)

Free ascorbic, dehydroascorbic (DHA), and total ascorbic (TA) acidcontent was determined for pre-cut Romaine lettuce using HighPerformance Liquid Chromatography (HPLC). Initial storage was 5 days at3.3° C., 80% R.H. in the dark, followed by placement of half the samplesat 5° C., 80% R.H under constant light, while half the samples remainedat this same temperature in total darkness. The temperature/light regimewas based on the observed environments for pre-cut products shipped fromCalifornia to the East Coast as discussed in Zeng et al. “Growth ofEscherichia coli O157: H7 and Listeria monocytogenes in PackagedFresh-Cut Romaine Mix at Fluctuating Temperatures During CommercialTransport, Retail Storage, and Display,” Journal of Food Protection®77(2):197-206 (2014), the disclosure f which is hereby incorporatedherein by reference in its entirety. In most cases, relative humidityduring shipping, distribution, and retail display is not controlled;however, it was controlled in this study to prevent excessivedehydration of tissues during storage. Packages were sampled at 0, 5, 8,11, 14, 17, and 21 days of storage.

HPLC analysis was performed following the method reported by Chebrolu etal. “An Improved Sample Preparation Method for Quantification ofAscorbic Acid and Dehydroascorbic Acid by HPLC,” LWT-Food Science andTechnology 47(2):443-449 (2012), the disclosure of which is herebyincorporated herein by reference in its entirety. A 2 g sample of tissuewas extracted with an equal weight of metaphosphoric acid (MA) (3 g/100ml DI water), using a tissue homogenizer. The sample was centrifuged at4500 revolutions per minute for 10 minutes after which the supernatantwas passed through a 0.45 μm syringe filter in preparation for HPLCanalysis. Likewise, tris(2-carboxy ethyl) phosphine hydrochloride (TCEP)was used to reduce DHA to ascorbic acid to determine DHA and totalascorbic acid content of the samples. A 2 g sample was treated with anequal weight of 3 g/100 ml MA, homogenized, and centrifuged. A 300 μLaliquot was treated with 300 of 5 mmol/L TCEP, incubated for 30 min,then filtered through a 0.45 μm filter before injection on the HPLC. Thesample DHA level was calculated as the difference between free AA andTA.

The HPLC used was a Shimadzu HPLC system equipped with photodiode arrayand C18 spherisorb column (150 mm×4.6 mm i.d. and 3 μm particle size)held at 25° C. The primary detection wavelength was 254 nm. Runs wereperformed isocratically using 0.01 mol/L dihydrogen ammonium phosphate(pH 2.6) as the mobile phase. Flow rate was 1 ml/min. Standards of 1 to10 mg ascorbic acid/100 ml MA solution were prepared from a stocksolution of 100 mg ascorbic acid/100 ml MA and were used to construct acalibration curve. The concentration of ascorbic, dehydroascorbic, andtotal ascorbic acid in the samples were expressed as mg per 100 g freshweight lettuce. Percent recovery was determined by taking two 10.00 gsamples of lettuce from a lettuce head. One sample was spiked with 0.1 gascorbic acid. Both samples were prepared and analyzed using theprocedure described above. This procedure was repeated 4 times.

Pro-Vitamin a (β-Carotene)

Pre-cut sweet potatoes were used as a model system. They are high inβ-carotene and do not contain related carotenoids, such as chlorophyll,that would require excessive processing to remove. The potatoes werestored as smooth-cut chips, julienned strips (“matchsticks”), fry-sizedwedges, and ˜½″ diced pieces. Pre-cut potatoes were treated with 100 ppmactive chlorine at pH 6.5 before being packaged to reduce decay duringstorage. Initial storage was 5 days at 3.3° C., 80% R.H in the dark,followed by placement of half the samples at 5° C., 80% R.H underconstant light, and half the samples at this same temperature in totaldarkness. Packages were sampled at 0, 5, 8, 11, 14, 17, and 21 days ofstorage and unwashed potatoes were sampled at Day 0, as well.

Beta-carotene was extracted using hexane and quantifiedspectrophotometrically as discussed in Picha, D. H., “HPLC Determinationof Sugars in Raw and Baked Sweet Potatoes.” Journal of Food Science50(4):1189-1190 (1985), the disclosure of which is hereby incorporatedherein by reference in its entirety. Peeled sweet potatoes wereinitially puréed using a small, table-top food processor. A 0.5 g sampleof the purée was extracted for 1 min with 10 ml of HPLC-grade hexane,using a tissue homogenizer. The homogenate was filtered through Whatman#1 paper into a 50 ml volumetric and additional hexane was added toyield a final volume of 50 ml. The absorbance of 3 ml of sample wasimmediately read at 440 nm. A β-carotene calibration curve was preparedusing 99.5% pure β-carotene. Results were expressed as mg β-carotene/100g fresh weight. β-Carotene makes up 86 to 90% of the carotenes presentin sweet potatoes, so changes at this wavelength were correlated withchanges in the nutritive value of the roots.

Chlorophyll

Previous research indicated that chives, when exposed to fluorescentlight, yellowed and senesced at a much faster rate than chives stored indarkness. Since the reduction in chlorophyll is so dramatic, chives wereused as the model system for this work. Initial storage was 5 days at3.3° C., 80% R.H. in the dark, followed by placement of half the samplesat 5° C., 80% R.H under constant light, and half the samples at thissame temperature in total darkness. Punnets were sampled at 0, 5, 8, 11,14, 17, and 21 days of storage.

To prevent the photodegradation of chlorophyll, all glassware wascovered with aluminum foil. From each sample 25 g of chives was weighedand placed in a blender. The tissue was homogenized for 1 min with 50 mlof deionized water. Ten grams of homogenized tissue was weighed into abeaker and 50 ml of 80% acetone:water (v/v) added as disclosed inYamauchi et al., “Chlorophyll and Xanthophyll Changes in BroccoliFlorets Stored Under Elevated CO2 or Ethylene-Containing Atmosphere,”HortScience 33(1):114-117 (1998) and Yamauchi et al., “Ascorbic Acid andBeta-Carotene Affect the Chlorophyll Degradation in Stored Spinach(Spinacia oleracea L.) Leaves,” Food Preservation Science 24(1):17-21(Japan) (1998), the disclosures of which are hereby incorporated hereinby reference in their entirety.

The sample was allowed to rest for 10 min. The extract was filteredthrough Whatman P8 filter paper using a vacuum system. Two 10 mlaliquots of 80% acetone:water were used to rinse the extraction beakerand solids remaining on the filter paper to ensure full extraction ofthe chlorophyll. The filtrate was further clarified by passing itthrough a non-reactive PTFE 0.45 μm syringe filter. Total chlorophyllwas measured using a spectrophotometer set at 645 nm.

Example 2 Materials and Methods

Sample Preparation:

Virgin PET resin and washed post-consumer (PC)-PET flake were blendedutilizing an industrial extruder to produce 0, 25, 50, 75, and 100%PCR-PET by weight. Briefly, washed post-consumer flake was transferredvia vacuum lines to a Con Air crystallizer and crystallized for 45minutes utilizing an air temperature of 155° C. The crystallizedPC-flake was blended with virgin resin in an AEC Whitlock OS seriesblender then transferred to a Con Air carousel drier; the blendedmaterial was dried for four hours at 140° C. with a −40° C. dew point.The dried material blend was transferred to a Reifenhäuser single screwextruder with a screw L/D ratio of 32:1 and extruded with a screw speedof 80 rpm. Film thicknesses for each sheet blend ranged between 18 miland 20 mil (0.457 mm and 0.508 mm, respectively).

Absorption Spectroscopy:

Film samples were fitted into a custom made clamp measuring 12.7 cm×8.5cm×2.3 cm. Ultraviolet-visible (UV-vis) spectra were collected between200 nm and 500 nm in absorbance mode using a Tecan Safire spectrometer(Zurich, Switzerland). Specimen thicknesses were measured with aMitutoyo IP 65 electronic digital micrometer (Kawasaki, Japan). Eachspecimen clamp was inserted into the sample chamber such that thespecimen was aligned perpendicular to the incident irradiation. EachUV-vis absorbance value measured along the spectrum was divided by thethickness of the specimen to account for differences in path lengthattributed to the variations in the thickness of each specimen betweenspecimens.

Vibrational Spectroscopy:

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR)spectra were collected with a Nicolet 380 Infrared spectrometer atambient temperature (Waltham, Mass.), fitted with a diamond crystalstage. The spectrum of each specimen was collected with 64 scans and aresolution of 2 cm-1.

Raman spectra were collected at ambient temperature on an XploRa Plusconfocal Raman microscope (Horiba Scientific/JY, France) using a 100×magnification, 0.90 numerical aperture microscope objective. Eachspectrum was comprised of three exposures with ten seconds per exposureand a resolution of 2 cm⁻¹. The excitation wavelength was 532 nm with alaser power of 10 mW. Each spectrum was baseline corrected and thechanges in area of each functional group characteristic band wascalculated after normalization to the area of the 705 cm⁻¹ bandaccording to the procedure (equation 1) described in Richard-Lacroix etal., “Orientation and Structure of Single Electrospun Nanofibers ofPoly(ethylene terephtalate) by Confocal Raman Spectroscopy.”Macromolecules 45:1946-53 (2012), the disclosure of which is herebyincorporated by reference in its entirety.

A(v)_(subtracted) =A(v)_(% RPET) −A(v)_(virgin RPET)  (1)

Fluorescence Spectroscopy:

Fluorescence intensity measurements were collected with a Tecan Safirefluorometer (Zurich, Switzerland). Scans were performed in 3Dfluorescence mode with excitation and emission wavelengths ranging from300 nm to 600 nm in 5 nm increments at a gain of 75. The z-position washeld fixed at 11,020 nm.

Statistical Analysis: Investigations for linear relationships and thestatistical significance of trendline slopes between data sets wereperformed by calculating the Pearson product moment correlationcoefficient and linear regression analysis, respectively, via Minitab 17software utilizing a 95% confidence (α=0.05) as described in Ellison,Practical Statistics for the Analytical Scientist: A Bench Guide. 2ndEd. Cambridge: The Royal Society of Chemistry (2009), which is herebyincorporated by reference in its entirety. Statistical comparisonbetween trendline slopes of the current data and the previous workdescribed in Curtzwiler et al., “Effect of Recycled Poly(ethyleneterephthalate) Content on Properties of Extruded Poly(ethyleneterephthalate) Sheets,” J. Plast. Film Sheeting 27:65-86 (2011), whichis hereby incorporated by reference in its entirety, was conducted bydetermining the interaction effect as described in Gelman, Data AnalysisUsing Regression and Multilevel/Hierarchical Models, New York, New York:Cambridge University Press (2007).

Results

Ultraviolet Visible Spectroscopy:

Virgin and PCR-PET polymer intrinsically absorbs UV radiation atwavelengths between 280 and 320 nm, which saturated the absorptiondetector utilizing the sample thickness (˜20 mil), common for packagingapplications as shown in FIG. 12A. Therefore, this region of the UVspectrum will not be discussed in reference to PCR-PET blendconcentrations. The UVA absorption potential for each PCR-PET blendratio was quantified and defined as the area under thethickness-normalized absorbance spectrum between 320 and 400 nm (UVAregion; FIG. 12A). The UVA absorption potential increased linearly withincreasing PCR content in the PCR-PET blend as shown in FIG. 12B and thedata indicated that utilizing 100% PCR-PET polymer increased the UVAabsorption by ˜100% for the current study and ˜80% in the previous work_ENREF_2 described in Curtzwiler et al., “Effect of RecycledPoly(ethylene terephthalate) Content on Properties of ExtrudedPoly(ethylene terephthalate) Sheets,” J. Plast. Film Sheeting 27:65-86(2011), compared to the virgin resin as shown in FIG. 1B. No significantdifference was determined between the data sets collected in 2011 andthe data reported here for the UVA absorption potential as a function ofPCR-PET concentration trendline slopes (p=0.129) nor the constant(p=0.09) of the regression equation (FIG. 12B), indicating thereproducible nature of UVA absorption properties for PCR-PET blends.

The thickness-normalized absorption at 350 nm was additionally evaluatedas a potential single point measurement to predict the UVA absorptionpotential as defined above. Pearson's product moment correlationcoefficient indicated a linear relationship between the A350 nm/mil andthe UVA absorption potential for both the 2011 and 2016 data setsindependently (p<0.001 and p=0.009, respectively). Regression analysisand investigation of the interaction effect of when the data werecollected (2011 compared to 2016) indicated that there was nostatistical difference between the trendline slopes (66.96 for 2011;68.91 for 2016; p=0.873), and thus, values from both data sets (2011 and2016) were combined and utilized to produce a regression equationcapable of providing a single measurement investigation of the UVAabsorption potential (R2=0.9289; Equation 2, FIG. 12c ); such anequation increases the viability using a single point, continuousmeasurement to monitor the UVA absorption performance of PCR-PET blendsand provides a quality control calibration parameter for adjusting thecomposition in real-time.

$\begin{matrix}{{{UVA}\mspace{14mu} {Absorption}\mspace{14mu} {Potential}} = {{68.46\; \frac{A_{350\mspace{14mu} n\; m}}{mil}} + 0.346}} & (2)\end{matrix}$

Vibrational Spectroscopy:

Understanding and determining the mechanism of increased UV absorbancewould enable converters and recyclers to reproducibly tailor blendcompositions and processing parameters to selectively control theultraviolet and visible light absorption properties. Accordingly, themolecular composition of each PCR-PET blend was investigated byvibrational spectroscopic methods. Both ATR-FTIR and Raman spectraindicated an overall decrease in carbonyl concentration for specimenscontaining PCR as shown in FIGS. 13A-13E. This is attributed to the lossof acetaldehyde/benzaldehyde degradation byproducts as described inSamperi et al., “Thermal Degradation of Poly(ethylene terephthalate) atthe Processing Temperature,” Polymer Degradation and Stability, 83:3-10(2004) and Holland, et al., “The Thermal Degradation of PET andAnalogous Polyesters Measured by Thermal Analysis—Fourier TransformInfrared Spectroscopy,” Polymer, 43:1835-47, which are herebyincorporated by reference in their entirety.

_ENREF_14 However, these spectroscopic methods do not easilydifferentiate carbonyl types, thus, quantification of the carbonyldecrease due to the loss of aldehyde species is confounded by theformation of quinone derivatives on the terephthalate constituents asdescribed in Romão et al., “Poly (ethylene terephthalate)Thermo-Mechanical and Thermo-Oxidative Degradation Mechanisms,” PolymerDegradation and Stability 94: 1849-59 (2009) and MacDonald, “NewAdvances In Poly(ethylene terephthalate) Polymerization andDegradation,” Polymer International, 51: 923-30 (2002), which are herebyincorporated by reference in their entirety. A reduction in theasymmetric ether stretch frequency at 1095 cm⁻¹ was also observed, whichmay be due to the degradation of the diethylene glycol substituents inthe PET backbone (FIGS. 13A-13E). It is noted that the reduction of theester stretch peak area from 1300 to 1200 cm⁻¹ was similar to thecarbonyl decrease and can be attributed to the concerted degradationmechanism through an unstable β-chain scission intermediate.

The reduction of the carbonyl, ester, ether, and C═C functional groupsas a function of PCR concentration deviated from linearity for the 50%PCR-PET blend as determined via ATR-FTIR and Raman spectroscopy (FIGS.13A-13E). This suggests that the 50% PCR-PET blend samples has uniquematerial properties that differ from the other blend ratios. At 50 wt %PCR-PET there is a 1:1 mass ratio between the virgin resin and PCRresin, but not a 1:1 molar ratio with respect to the average molecularweight and polydispersity. It is well established in the literature thatmelt processing decreases the molecular weight of PET polymer due to thethermal and mechanical forces acting on the polymer during processing asdiscussed in Andrassy et al., “Molecular Mass Distribution ChangesDuring Processing of Poly(ethylene terephthalate),” Polymer degradationand stability, 41: 77-81 (1993), Awaj a et al., “Recycling of PET,”European Polymer Journal, 41: 1453-77 (2005), and Incarnato et al.,“Structure and Rheology of Recycled PET Modified by Reactive Extrusion,”Polymer, 41: 6825-31 (2000), which are hereby incorporated by referencein their entirety.

During melt processing of the material, it is possible that thedissimilar molecular weight molar ratio between the virgin material andthe PCR-PET creates heterogeneity in the material (e.g., phaseseparation/domains of various molecular weights or less perfectcrystalline structures). Since PCR-PET has a higher crystalline fractionthan virgin PET under the same extrusion/take up conditions (as measuredvia differential scanning calorimetry)_ENREF_21 as discussed inCurtzwiler et al., “Effect of Recycled Poly(ethylene terephthalate)Content on Properties of Extruded Poly(ethylene terephthalate) Sheets,”J Plast Film Sheeting, 27: 65-86 (2011), which is hereby incorporated byreference in its entirety, heterogeneity would result in a preferentialsegregation of PCR-PET forming a heterogeneous mixture of each materialin the final blend, which would be most noticeable at 50 wt % PCR-PET.The resulting dispersion would result in more stability of the aromaticring through ordered structure, and a greater crystalline fraction inthe CH2 deformation vibration between 720 and 725 cm⁻¹. Analysis of thecrystalline fraction of the 50 wt % PCR-PET via ATR-FTIR revealed anincrease in the concentration of the crystalline fraction over that invirgin PET resin and the other PCR-PET samples. The increase in thecrystalline CH₂ deformation domain supports the possibility of aheterogeneous dispersion with the 1:1 blend ratio as illustrated in FIG.14.

Fluorescence Spectroscopy:

Thermo-mechanical processing/reprocessing of PET induces main chain andend group degradation that results in the production of fluorescentquinone derivatives due to radical attack of the terephthalic acidaromatic ring as described in MacDonald, “New Advances in Poly(ethyleneterephthalate) Polymerization and Degradation,” Polymer International,51: 923-30 (2002), which is hereby incorporated by reference in itsentirety. A strong coefficient of determination (R2=0.9878) wasdetermined between the absorbance at 350 nm (UV-Vis spectroscopy) andthe fluorescence intensity at 501 nm (ex 350 nm), suggesting that themoiety causing the increased absorbance at 350 nm (as noted in FIGS.12A-12C) is due to the quinone derivatives formed during degradationprocesses. This trend is expected as increasing PCR content increasesthe mass fraction of material that has undergone multiple meltprocessing steps increasing the potential for degradation reactions.Although this study proposes that fluorescence properties with anexcitation wavelength of 350 nm and emission at 501 nm are due to thequinone derivatives, the 3D fluorescence scans revealed that theseconditions do not represent the fluorescence intensity maximum for eachPCR-PET blend.

3D fluorescence scans were utilized to identify the peak fluorescentintensity of each PCR-PET blend as shown in FIG. 15. The highestfluorescence intensity maximum for all PET samples occurred at anexcitation wavelength of 335 nm and an emission wavelength of 395 nm.Increased fluorescence under these excitation and emission parametershave been previously attributed to the formation of dimers due tointramolecular interactions of non-nearest neighbor phenyl rings orintermolecular interactions between adjacent chains resulting in dimersas described in Sonnenschein et al., “Absorption and FluorescenceSpectra of Poly(ethylene terephthalate) Dimers,” Polymer, 31: 2023-60(1990) and Dodge et al., “Conformation of the Ground-State Dimer inPoly(ethylene terephthalate),” Journal of Polymer Science Part B:Polymer Physics, 31: 207-12 (1993), which are incorporated by referencein their entirety. While it was anticipated that increasing PCR-PETcontent would increase the observed peak fluorescent intensity due tothe formation of more quinone derivatives, the 50 wt % PCR-PET blend asshown in FIG. 16, possessed the highest peak intensity among allmeasured content which is inversely proportional to the trends noticedvia Raman and ATR-FTIR spectroscopies; despite the UVA absorptionpotential increases having been attributed to the formation of quinonederivatives as noted above, the increase in peak fluorescenceintensities is likely attributed to dimer formation which iscorroborated by the increased stability of the aromatic ring due topi-pi stacking also observed in the ATR-FTIR analysis. These resultssuggest that fluorescence spectroscopy may be used to monitor theheterogeneity of the manufactured PCR-PET films.

Although preferred embodiments have been depicted and described indetail herein, it will be apparent to those skilled in the relevant artthat various modifications, additions, substitutions, and the like canbe made without departing from the spirit of the invention and these aretherefore considered to be within the scope of the invention as definedin the claims which follow.

What is claimed:
 1. A system comprising: an extrusion, injection, orresin conversion system configured to blend a plurality of resinfeedstocks to form a plastic composition; one or more sensors positionedat different locations in the extrusion system to measure one or moreproperties of the plastic composition, wherein the one or moreproperties of the plastic composition comprise one or more of radiationabsorption, radiation transmission, gas evolution, or radiationfluorescence; and a computing device comprising a processor and a memorycoupled to the process which is configured to execute one or moreprogrammed instructions stored in the memory and comprising: receivingmeasurements of the one or more properties of the plastic compositionfrom the one or more sensors; and outputting one or more instructions toadjust the ratio of the plurality of resin feedstocks being blended intothe plastic composition based on the measured one or more properties tooptimize the radiation transmission, the radiation absorption, aradiation emission, or a radiation reflection of the plastic compositionto form an optimized plastic composition; and a compound delivery systemconfigured to adjust the plurality of resin feedstocks based on themeasured one or more properties.
 2. The system of claim 1 furthercomprising: at least two sensors located at different locations in theextrusion, injection, or resin conversion system.
 3. The system of claim1, wherein the one or more sensors are designed to perform one or moreof an ultraviolet-visible spectroscopy analysis, an attenuated totalreflectance Fourier transform infrared spectroscopy analysis, adifferential scanning calorimetry analysis, a mechanical analysis, x-rayfluorescence analysis, or energy dispersive x-ray fluorescence analysis.4. The system of claim 1, wherein the compound delivery system comprisesone or more of a co-extruder, a dosing pump, or a direct intake to anextrusion line.
 5. The system of claim 1, wherein the receivedmeasurements of the one or more properties of the plastic compositionare carried out at least two different points in time following saidblending.
 6. The system of claim 1, wherein the outputting the one ormore instructions comprises outputting one or more instructions toadjust the ratio of the plurality of resin feedstocks so the optimizedplastic composition isolates and controls electromagnetic wavelengthsassociated with one or more of vitamin degradation, adverse colorchanges, chlorophyll degradation, or degradation of other nutritionalcomponents.
 7. The system of claim 1, wherein the outputting the one ormore instructions comprises outputting one or more instructions toadjust the ratio of the plurality of resin feedstocks so the optimizedplastic composition absorbs 50% to 75% more incident ultraviolet lightcompared to composition feedstocks that do not contain recycled content.